Northeastern University College of Engineering

Bruno Souto Maior Muniz Morais PhD Dissertation Defense
2023-11-27 08:00 - 17:00 (America/New_York)
Title:
Enabling Domain Platform Design for Streaming Applications: A Holistic Approach
Committee Members:
Gunar Schirner (Advisor)
Prof. David Kaeli
Prof. Hamed Tabkhi (UNCC)
Time:
10:00:00 AM
Location: ISEC 601
Abstract:
In recent years, more demanding streaming applications make striking a balance between high compute performance and efficiency paramount in platforms designs for edge computing. In addition, designing a platform that is optimized for a single application is costly due to non-recurring engineering (NRE) costs. In contrast, multiple applications can be grouped in domains, e.g. computer vision, software-defined radio. Leveraging shared characteristics of similar applications within a domain, e.g. structural composition/computation patterns, a single domain platform that caters to these similarities and accelerates applications can be generated, thus benefiting multiple applications at once and dramatically improving NRE and time-to-market (TTM).
This dissertation introduces methodologies atvarious abstraction levels to enable streamlined domain platform design for streaming applications. Thrust 1 introduces high level DSE methods based on integer linear programming (ILP), Tile-based Synchronization Aware ILP (TSAR-ILP). Initially, single-application platform allocations are considered using TSAR-ILP. While TSAR-ILP only focuses on applications in isolation, its formulation lays the foundations for DmTSAR-ILP, a method that performs domain DSE with multiple applications, obtaining an optimal unified platform allocation that and achieving an increase of 22.5% in throughput, while being 70x faster when compared to previous methods (MG-DmDSE). However, DmTSAR-ILP aims to aggregate all applications fairly. This presents a challenge when the designer wishes to focus on a subset of applications. To enable ultimate flexibility in a product-oriented setting, modeled after a market analysis process, this dissertation introduces ProdDSE. ProdDSE enables application prioritization while also introducing concurrent application modeling and a multi-objective optimization (area, performance) approach. This enables up to a 3.4x boost in performance depending on use case, while also providing gains in DSE runtime (4.3x faster).
Thrust 2 introduces Sedona, a domain-specific language (DSL) and exploration enviroment that captures parametric dataflow application descriptions with language features dedicated to streaming applications. A design identified by Thrust 1 can be further refined using the tools in Thrust 2, by capturing the connectivity of a design using Sedona. Then, automatic wiring is performed for target outputs such as timing-aware simulations or RTL-level code, enabling structural manipulation at a high-level description without the burden of low-level manual integration.
Finally, to better guide the high-level decisions performed in Thrust 1 and further exploration/integration in Thrust 2, Thrust 3 considers the implications of HWACC topology choices in an HWACC-rich SoC. The ACTAR flow is introduced to explore different topologies in a RISC-V based SoC and the side-effects of topology and memory sizing choices on the system-wide performance and synchronization burdens due computation offloading to HWACCs. This produces valuable and actionable insights for designers to make informed choices on system-level compositions depending on application communication and computation demands.

Chemical Engineering Fall Seminar Series: Professor Haotian Wang
2023-11-29 12:00 - 13:00 | 010 Behrakis, 360 Huntington Ave, Boston, MA, 02115, United States
Electrochemical Approaches to Decarbonizing Fuels and Chemicals
Electrochemical conversion of atmospheric molecules (CO2, O2, H2O, N2) into fuels and chemicals represents a green and alternative route compared to traditional manufacturing approaches. However, its practice is currently challenged at two systematic levels: the lack of active, selective, and stable electrocatalysts for efficient and reliable chemical bond transformations, and the lack of novel catalytic reactors for practical reaction rates and efficient product separation. In this talk, using CO2 reduction to gas and liquid products and O2 reduction to hydrogen peroxide as representative reactions, I will introduce the rational design of both catalytic materials and reactors towards practical electrochemical manufacturing of fuels and chemicals.

Dr. Haotian Wang is currently an Associate Professor in the Department of Chemical and Biomolecular Engineering at Rice University. He obtained his PhD degree in the Department of Applied Physics at Stanford University in 2016 and his Bachelor of Science in Physics at the University of Science and Technology of China in 2011. In 2016 he received the Rowland Fellowship and began his independent research career at Harvard as a principal investigator. He was awarded the 2021 Sloan Fellow, 2020 Packard Fellow, 2019 CIFAR Azrieli Global Scholar, 2019 Forbes 30 Under 30, highly cited researchers, etc. He serves as the editorial board of Communications Materials. His research group has been focused on developing novel nanomaterials for energy and environmental applications including energy storage, chemical/fuel generation, and water treatment.

Aria Masoomi PhD Proposal Review
2023-11-29 15:00 - 16:00
Title:
Making Deep Neural Network Transparent
Date:
11/29/2023
Time:
3:00:00 pm
Committee Members:
Prof. Jennifer Dy (Advisor)
Prof. Mario Sznaier
Prof. Eduardo Sontag
Prof. Peter Castaldi
Abstract:
As machine learning algorithms are deployed ubiquitously to a variety of domains, it is imperative to make these often black-box models transparent.
The ability to interpret and comprehend the reasoning behind machine learning models plays a pivotal role in increasing user trust. It not only offers insights into how a model functions but also opens avenues for model enhancements.
This research delves into the realm of interpretability, focusing on the dichotomy between ‘intrinsic’ and ‘post hoc’ interpretability. Intrinsic interpretability involves constraining the complexity of the machine learning model itself, resulting in models inherently interpretable due to their simplicity, such as decision trees or sparse linear regression. On the other hand, post hoc interpretability employs techniques that assess the model’s behavior after training, offering insights into the model’s outcomes. Examples of post hoc techniques include permutation feature importance and the Shapley value method for feature importance.
The core contribution of this Thesis proposal lies in the development of novel methods to enhance both intrinsic and post hoc interpretability. These methods aim to advance the field by offering new perspectives on understanding machine learning models, thereby contributing to the ongoing discourse on model transparency and user trust.

Aria Masoomi PhD Proposal Review
2023-11-29 16:30 - 17:00
Title:
Making Deep Neural Network Transparent
Date:
11/29/2023
Time:
4:30:00 PM
Committee Members:-
Prof. Jennifer Dy
Prof. Eduardo Sontag
Prof. Mario Sznaier
Prof. Peter Castaldi
Abstract:
As machine learning algorithms are deployed ubiquitously to a variety of domains, it is imperative to make these often black-box models transparent. The ability to interpret and comprehend the reasoning behind machine learning models plays a pivotal role in increasing user trust. It not only offers insights into how a model functions but also opens avenues for model enhancements.
This research delves into the realm of interpretability, focusing on the dichotomy between ‘intrinsic’ and ‘post hoc’ interpretability. Intrinsic interpretability involves constraining the complexity of the machine learning model itself, resulting in models inherently interpretable due to their simplicity, such as decision trees or sparse linear regression. On the other hand, post hoc interpretability employs techniques that assess the model’s behavior after training, offering insights into the model’s outcomes. Examples of post hoc techniques include permutation feature importance and the Shapley value method for feature importance.
The core contribution of this Thesis proposal lies in the development of novel methods to enhance both intrinsic and post hoc interpretability. These methods aim to advance the field by offering new perspectives on understanding machine learning models, thereby contributing to the ongoing discourse on model transparency and user trust.

Gordon Institute Virtual Information Session
2023-11-29 17:30 - 18:30
Learn how you can earn a Graduate Certificate in Engineering Leadership as a stand-alone certificate or in combination with one of twenty-three Master of Science degrees offered through Northeastern’s College of Engineering, College of Science, or Khoury College of Computer Sciences.
The National Academy of Engineering recognized The Gordon Institute of Engineering Leadership (GIEL) for its innovative curriculum that combines technical education, leadership capabilities, and the “Challenge Project”: an opportunity for students to receive master’s level credit while working in industry.
By aligning technical proficiency with leadership capabilities, GIEL accelerates the development of high-potential engineers and prepares them to lead complex projects early in their careers. Upon completing the program, more than 88% of the 2022 class reported increased leadership responsibility, while more than 50% of the 2022 class reported being promoted within one year of graduation.
Our Director of Admissions will answer your application questions for Fall 2024.
You will have the opportunity to hear from Alumni on how The Gordon Institute propelled their engineering careers. Program professors will also be present to answer curriculum questions.

First-Year Engineering Fall Expos
2023-12-01 09:00 - 12:00 | Curry Student Center, 360 Huntington Ave., Boston, MA, 02115, United States
Join us for First-Year Engineering’s Fall Expos on Friday, December 1, from 9:00 AM – 12:00 PM and Monday, December 4, from 11:00 AM – 3:00 PM in the Curry Student Center Pit and Quad. Cornerstone of Engineering students will be showcasing their Fall projects. Themes include sumo robots, sustainability, carnival games, animals and the natural world, and interactive games.

Cheng Gongye PhD Dissertation Defense
2023-12-04 10:30 - 11:30
Title:
Hardware Security Vulnerabilities in Deep Neural Networks and Mitigations
Date:
12/4/2023
Time:
10:30:00 AM
Committee Members:
Prof. Yunsi Fei (Advisor)
Prof. Aidong Ding
Prof. Xue Lin
Prof. Xiaolin Xu
Abstract:
In the past decade, Deep Neural Networks (DNNs) have become pivotal in numerous fields, including security-sensitive autonomous driving and privacy-critical medical diagnosis. This Ph.D. dissertation delves into the hardware security of DNNs, discovering their vulnerabilities to fault and side-channel attacks and exploring novel countermeasures essential for their safe deployment in critical applications.
Fault attacks disrupt computation or inject faults into parameters, compromising the integrity of targeted applications. This dissertation demonstrates a power-glitching fault injection attack on FPGA-based DNN accelerators, common in cloud environments, which exploits vulnerabilities in the shared power distribution network and results in model misclassification. In response to these threats, we introduce a novel, lightweight defense mechanism to protect DNN parameters from adversarial bit-flip attacks. The proposed framework incorporates a dynamic channel-shuffling obfuscation scheme coupled with a logits-based model integrity monitor. The approach effectively safeguards various DNN models against bit-flip attacks, without necessitating retraining or structural changes to the models. Furthermore, our research expands the scope of fault analysis beyond just the parameters of DNN models. We thoroughly examine the entire implementation of commercial products, defying the prevailing assumption that quantized DNNs are inherently resistant to bit-flips.
Side-channel attacks exploit information leakage of system implementations, such as power consumption and electromagnetic emanations, to reveal system secrets and therefore compromise confidentiality. This dissertation makes significant contributions to side-channel assisted model extraction of DNNs. We present a floating-point timing side-channel attack on x86 CPUs that reverse-engineers DNN model parameters in software implementations. For hardware accelerators, we target the state-of-the-art AMD-Xilinx deep-learning processor unit (DPU), a reconfigurable engine dedicated to convolutional neural networks (CNNs) and representing the most complex commercial FPGA accelerator with encrypted IPs. Our work demonstrates that electromagnetic analysis can be leveraged to recover the data flow and scheduling of the DNN accelerators, facilitating follow-on architecture and parameter extraction attacks. To mitigate EM side-channel model extraction attacks, we introduce a novel defense mechanism that devises a random importance-aware activation mask on input pixels to disrupt the operation alignment on EM traces, with minimal performance and efficiency impacts.
Overall, this dissertation significantly deepens the understanding of hardware security of DNN models. It makes important contributions in discovering novel and critical vulnerabilities of DNN inference pertaining to system implementations, and proposing effective and practical solutions for securing DNNs in mission-critical environments. The research work marks a substantial step forward in the development of resilient and secure AI systems.

Chemical Engineering Fall Seminar Series: Professor Cameron Abrams
2023-12-06 12:00 - 13:00 | 010 Behrakis, 360 Huntington Ave, Boston, MA, 02115, United States
Molecular Dynamics Investigations of Thermosetting Polymers
Thermosetting polymers comprise a wide variety of monomer constituents and polymerization chemistries that in principle provide the degrees of freedom necessary to tailor these materials to a broad range of applications, from structural composites, coatings and barrier materials, ballistic shielding, and even solid rocket fuels. In this talk, I will trace my group’s history in using molecular dynamics simulations to investigate conceptual links among molecular architectures, intermolecular interactions, and network structures and how they determine thermomechanical properties of polymerized materials that these applications demand. Highlights in this history include the discovery of the links between crosslink arrangements and protovoid-based toughening; toughening using partially reacted substructures; long-timescale material response through time-temperature superposition; and rationalizing improvements over petrochemically derived monomers using novel bio-based subunits. A consistent theme will be demonstration of how close collaboration with experimental groups allows for simulation predictions to be tested. I will conclude with a presentation of our group’s software package, HTPolyNet, that represents the first opensource, end-to-end generator of all-atom models of network-polymerized monomer mixtures based only on monomer structures, which should accelerate the community’s use of MD simulation to investigate thermosetting polymers.

Cameron F. Abrams is the Bartlett ’81 – Barry ’81 Professor of Chemical and Biological Engineering at Drexel University, where he has served on the faculty since 2002 and as Department Head since 2017. Abrams’ research expertise lies in advancing modern molecular simulation methods with applications in protein science, drug discovery, complex fluids, and high-performance materials. He is the recipient of an ONR Young Investigator Award, an NSF CAREER Award, and the AIChE Computational and Molecular Sciences Forum Impact Award. He received a BS in Chemical Engineering from North Carolina State University in 1995 and a PhD from the University of California, Berkeley, in 2000. He trained as a postdoc for two years in the Theory Group at the Max-Planck-Institute for Polymer Research in Mainz, Germany, before joining Drexel.

Enabling Engineering Fall Showcase
2023-12-06 13:00 - 15:00 | 002 Ell Hall, 360 Huntington Ave, Boston, MA, 02115, United States
Students will present their final design projects at the Enabling Engineering Fall Showcase. The projects that will be presented are listed below.

Accessible Golfing
Caution Radar
Accessible Plant Watering System
Switch Activated Cornhole
VR App
Switch Activated Toys
Adaptive Drum Set
Prosthetic Finger

Enabling Engineering is a Northeastern University student group that designs and builds devices to empower individuals with physical and cognitive disabilities. Our students collaborate with clients on projects that provide greater independence, reduce medical burdens, and increase social connectedness. We help family members, clinicians, and teachers care for people with disabilities. By giving students the opportunity to participate in Enabling Engineering projects, we are training the next generation of engineers to be knowledgeable about, and aware of, the needs of individuals with disabilities.
If you are unable to join in person, you can join via Zoom.

Suyash Pradhan MS Thesis Defense
2023-12-06 15:00 - 17:00
Title: COPILOT: Cooperative Perception using Lidar for Handoffs between Road Side Units
Committee Members:
Prof. Kaushik Chowdhury (Advisor)
Prof. Stratis Ioannidis
Prof. Jennifer Dy
Abstract:
This thesis presents COPILOT, a ML-based approach that allows vehicles requiring ubiquitous high bandwidth connectivity to identify the most suitable road side units (RSUs) through proactive handoffs. By cooperatively exchanging the data obtained from local 3D Lidar point clouds within adjacent vehicles and with coarse knowledge of their relative positions, COPILOT identifies transient blockages to all candidate RSUs along the path under study. Such cooperative perception is critical for choosing RSUs with highly directional links required for mmWave bands, which majorly degrade in the absence of LOS. COPILOT proposes three modules that operate in an inter-connected manner: (i) As an alternative to sending raw Lidar point clouds, it extracts and transmits low-dimensional intermediate features to lower the overhead of inter-vehicle messaging; (ii) It utilizes an attention-mechanism to place greater emphasis on data collected from specific vehicles, as opposed to nearest neighbor and distance-based selection schemes, and (iii) it experimentally validates the outcomes using an outdoor testbed composed of an autonomous car and Talon AD7200 60GHz routers emulating the RSUs, accompanied by the public release of the datasets. Results reveal COPILOT yields upto 69.8% and 20.42% improvement in latency and throughput compared to traditional reactive handoffs for mmWave networks, respectively

The Graduate School Collaborative Webinar: Why consider Graduate School?
2023-12-06 19:30 - 21:00
Think graduate school is just for future faculty? Think again!
Join us as we demystify what it takes to be and become a graduate student (MS and PhD), as well as the many career avenues that you can take with a graduate school degree. Come learn about the following topics from some of the top Engineering Graduate Programs in the country!

What is graduate school
What to expect
Why consider graduate school
How to prepare
Thinking ahead to the application process

What: Seminar on “Why consider Graduate School?”
When: Wednesday December 6th, 2023; 7:30 – 8:30 p.m. Eastern Time
For Who: Undergraduate Freshman, Sophomore, and Juniors
Sponsored by: UC Berkeley, Georgia Tech, John’s Hopkins university, University of Michigan, Northeastern, NYU, Ohio State, University of Oklahoma, Purdue University, Rice University, University of Southern California
Where: Zoom – Register using the Link below
Questions about accessing the webinar? Contact Nina Parshall at parshall.8@osu.edu

Note: There will be breakout sessions with each school for 30mins following the presentation (8:30pm – 9:00pm Eastern). Register to join for some exclusive networking!

Mauro Belgiovine PhD Proposal Review
2023-12-07 10:00 - 11:00
Title: Wireless Intelligence: A Comprehensive Exploration of AI-Driven Solutions in Channel Estimation, Beam Refinement, and Protocol Classification for Next Generation Networks
Committee Members:
Prof. Kaushik Chowdhury (advisor)
Prof. Stratis Ioannidis
Dr. Chris Dick
Abstract:
his thesis explores the transformative impact of artificial intelligence (AI) on wireless systems through model-driven simulations and real-world datasets, with a focus on enhancing both local and cellular wireless networks through the deployment of highly customized deep learning solutions that target specific bottlenecks affecting traditional signal processing based communication.
The research delves into three key areas that address critical challenges in the current wireless landscape. The first focal point of the investigation involves channel estimation using deep learning techniques to denoise pilots and expedite the accurate estimation of Channel State Information (CSI). By leveraging deep learning methodologies, the proposed solution aims to enhance the reliability and computation for MIMO and massive MIMO channel estimation, thereby contributing to improved communication efficiency and reduced errors. The second major topic encompasses the application of reinforcement learning for 5G New Radio (NR) millimeter-wave (mmWave) beam refinement. The study aims to develop a Deep Reinforcement Learning algorithm capable of adjusting beamsteering angles, starting from a coarse beam scanning procedure and further refining them for higher transmission efficiency. This innovation is expected to substantially decrease traffic overhead while simultaneously enhancing beam steering precision, thus optimizing the performance of mmWave communication. The third and final area of focus introduces a transformer-based WiFi multi-protocol classifier, strategically deployed on a DeepWave Air-T edge device, which is equipped with Module on Chip (MoC) low power CPU-GPU and programmable Software Defined Radio (SDR). This classifier outperforms existing modulation classification models and legacy methods under lower SNR conditions, leveraging TensorRT’s model compression capabilities to efficiently process extended sequences of raw IQ samples, ensuring high performance at a low computational cost. The proposed solution addresses the growing demand for efficient and adaptable wireless communication systems, paving the way for advancements in edge-based processing and intelligent protocol classification.
This work seeks to contribute significantly to the ongoing AI revolution in wireless systems by addressing crucial issues in channel estimation, beam refinement, and protocol classification. The outcomes of this research hold the potential to redefine the landscape of wireless communication, offering enhanced performance, reduced overhead, and increased adaptability in both local and cellular networks.

Yisi Liu MS Thesis Defense
2023-12-07 14:00 - 15:00
Title: Experimental research on the Nonlinear Magnetoelectric Effect of the VLF ME Antennas
Committee Members:
Prof. Nian Sun (Advisor)
Prof. Yongmin Liu
Prof. Xufeng Zhang
Abstract:
Magnetoelectric (ME) coupling effects in ferromagnetic and piezoelectric composites involve the control of electric polarization (P) by applying a magnetic field (H) (direct ME effect), or the manipulation of magnetization (M) through an electric field (E) (converse ME effect) . These effects are facilitated by the mechanical deformation in the ferroic phases resulting from the combination of magnetostriction and piezoelectricity. In single-phase materials, the breakthrough in achieving large ME coefficients has further advanced the development of ME materials and devices. Consequently, numerous multifunctional ME devices, such as mechanical antennas, magnetic sensors, tunable inductors, and filters, have been developed. This thesis has provides a summary and categorization of these devices based on their physical mechanism and type of ME effects. The inclusion of mechanical ME antennas based on piezoelectric/magnetostrictive heterostructures with acoustic actuation reflects the significant interest in this topic. Notably, a maximum communication distance of 120 m for a very low frequency (VLF) communication system has been achieved using a pair of mechanical ME antennas. Subsequently, we will focus on introducing and reviewing the materials and devices related to the ME effect, as well as the application of ME mechanical antennas in very low frequency (VLF) communication systems.
In addition to that, we developed a transmitter with a Metglas/PZT/Metglas structure antenna. Our study focuses on investigating the transmission effects of this antenna when employing direct antenna modulation techniques to enhance data transmission. Through our research, we have introduced a novel modulation method by modulating the antenna. We observed that this modulation method produces a more stable and stronger signal.

Ate Darabi PhD Proposal Review
2023-12-08 12:00 - 12:30
Title:
Complex Delayed Networks and Their Application in Epidemic Analysis: Modeling, Analysis, and Strategic Management
Committee Members:
Prof. Milad Siami (Advisor)
Prof. Bahram Shafai
Prof. Rozhin Hajian
Abstract:
In the face of crowd-related disasters like pandemics and mass attacks, the complex dynamics of human interactions demand comprehensive modeling approaches. This proposal adopts a network-based perspective, leveraging the delayed Susceptible-Infected-Susceptible (SIS) model for epidemics and the Predator-Swarm-Guide (PSG) model for crowd movement, to gain insights into the dynamics of these critical situations.
In epidemic networks, time delays and uncertainties can significantly change the epidemic behavior and result in successive echoing waves of the spread between various population clusters. We examine these effects on linear SIS dynamics, evaluating network stability and performance loss. We prove that network performance loss is correlated with the structure of the underlying graph, intrinsic time delays, epidemic characteristics, and external shocks. This performance measure is then used to develop an optimal traffic restriction algorithm for network performance enhancement, resulting in reduced infection in the metapopulation. An epidemic-based centrality index is also proposed to evaluate the impact of every subpopulation on network performance, and its asymptotic behavior is investigated. This index converges to local or eigenvector centralities under specific parameters. Moreover, given that epidemic-based centrality depends on the epidemic properties of the disease, it may yield distinct node rankings as the disease characteristics slowly change over time or as different types of infections spread. This unique characteristic of epidemic-based centrality enables it to adjust to various epidemic features. The derived centrality index is then adopted to improve the network robustness against external shocks on the epidemic network.
The PSG model addresses mass attack scenarios, considering individuals’ efforts to evade adversaries and seek guidance. Environmental factors like impermeable walls and psychological elements are incorporated into this model. The preliminary results highlight the role of coordinated cooperation in minimizing casualties. The objective is to reduce casualties through a hybrid motion optimization approach for individuals and the guiding agent.

Cookies with the Dean: Holiday Edition
2023-12-11 13:00 - 14:30 | The Tents at Robinson Quad, 336 Huntington Ave, Boston, MA, 02115, United States
Join Dean Gregory Abowd for a holiday-themed Cookies with the Dean on Monday, December 11th from 1:00-2:30pm in the Robinson Quad Tent near Curry Student Center.
We will have holiday cookies, hot cocoa, and free Swag available! Swing by for food, fun, and a chance to talk to the Dean.
We hope to see you there!
This is a 100% compostable event. Please place all waste into the marked composting bins for their disposal.

Durga Suresh PhD Proposal Review
2023-12-12 09:00 - 10:00
Title: Network Security Management and Threat Mitigation in the Open Cloud

Committee Members:
Prof. Miriam Leeser (Advisor)
Prof. Michael Zink
Prof. Xiaolin Xu
Abstract:
Cloud computing and advanced cyberinfrastructures are increasingly vital to the functioning of Internet systems. Every day, more devices are added to the cloud, to provide greater resource utilization, availability, and scalability. Due to the expanding reliance on cloud computing, securing the cloud is paramount. Tackling the issue of securing the cloud is crucial not only for preserving the functionality and reliability of cloud-based systems but also for protecting the critical data and services that depend on these platforms.
Cloud computing models include public clouds, private clouds, community clouds, and hybrid clouds. Private, community, and hybrid clouds provide security, but with an important trade-off; namely, user access restriction in the cloud. The proposed research uses the Open Cloud Testbed (OCT) which is part of the National Science Foundation’s (NSF) Computer and Information Systems Engineering(CISE) Community Research Infrastructure(CRI) program. OCT is an example of a public cloud that allows users two things: 1) an isolated set of nodes to perform experiments with bare metal access, which can potentially lead to security issues, and 2) the ability to test out the solutions for both using the cloud and adding security to it. The proposed research aims to target a system like the OCT, specifically targeting a public cloud environment.
This system will be designed to allow access to the switch, enabling control and management of traffic within the cloud network. This research aims to mitigate network security threats in the public cloud network. The aim of this research is multifold. First, we identify and classify the behavior of users in the cloud. We then provide an approach to creating a network security management policy that will deal with 1)detecting network intruders that scan the cloud network and remove their access to the network, and 2) managing heavy hitters that can cause Denial of Service (DOS) and Distributed Denial of Service (DDOS) attacks in the cloud network by using the heavy hitter detection system and prevent them from putting more traffic on the network. Both network intruder detection and heavy hitter management systems use Access Control Lists (ACL)as a means to prevent the user from putting traffic on the cloud network. Lastly, we perform experiments to handle these threats and measure the success of the experimental setup concerning network attacks. The proposed approach will ensure network security by creating a framework for network security management policy to minimize threats in the cloud network and other resources directly attached to the network. The proposed research aims to enhance cybersecurity by employing network intruder detection techniques to identify potential threats, implementing heavy hitter management to mitigate threats effectively, and developing and enforcing a network security management policy to prevent future threats.

Deepak Prabhala MS Thesis Defense
2023-12-12 11:00 - 17:00
Title: “Smart Microwave Devices with Programable Printed Circuit Board (PPCB): Design with Liquid Crystal Elastomer Polymers in Transmission Lines and Circulators”
Committee Members:
1) Professor Nian X. Sun (Advisor)
2) Professor Marvin Onabajo
3) Professor Yongmin Liu
Abstract:
This study explores the innovative application of liquid crystal elastomer (LCE) polymers in the design and implementation of microwave transmission lines and circulators. Liquid crystal elastomers, known for their unique combination of liquid crystalline and elastomeric properties, offer a unique approach to developing flexible and tunable microwave devices. The research focuses on a thorough study of the electro-mechanical properties of LCEs to achieve novel functionalities in the design of transmission lines and circulators for microwave communication systems in HFSS simulations. The first part of the study delves into the characterization of the dielectric and mechanical properties of the chosen LCE polymer. Subsequently, the design and fabrication of a flexible and tunable transmission line using LCE are discussed. The LCE-based transmission line aims to measure the insertion loss and return loss with different widths, lengths, and thicknesses of the LCE polymer. The study investigates the impact of temperature on the transmission line’s performance, offering insights into potential applications in reconfigurable microwave systems. The second phase of the research explores the utilization of LCE in the development of a microwave circulator, a vital component in microwave communication networks. The circulator design incorporates the unique properties of LCE by using a stepped dielectric variation approach for broadband isolation. This innovation holds promise for enhancing the efficiency and adaptability of microwave systems in communication and radar applications. The findings of this research contribute to offering a pathway for integrating liquid crystal elastomers into flexible and reconfigurable microwave devices. This thesis aims to advance the understanding of smart microwave devices and inspire further exploration into the application of liquid crystal elastomer polymers in cutting-edge technologies.

Night of yalda
2023-12-15 12:00 - 13:00
Honor the longest night of the year in this Persian cultural tradition to offer hope and light.
There will be watermelon, pomegranate, Persian food, and of course Persian music.
Not necessary, but we will be happy if you show up in red.
Location: 111 Snell Library

Xiang Zhang PhD Proposal Review
2023-12-20 14:00 - 15:00
Title:Confidentiality and Privacy Preserving: Intertwining Deep Learning and Side-channel Analysis
Meeting ID: 976 4324 8925 Passcode: [REDACTED]
Committee Members:
Prof. Yunsi Fei (Advisor)
Prof. Adam Ding
Prof. Lili Su
Abstract:
In the past decade, deep learning-empowered technologies have significantly permeated our daily lives, revolutionizing diverse application domains with superb performance. In hardware security, deep learning has been employed for power or electromagnetic side-channel analysis (SCA) and protection, and the security of deep learning implementations starts gaining traction.
This dissertation delves into the intertwining deep learning techniques and side-channel analysis. It addresses two critical questions: how to extend deep learning to other types of SCAs; what confidentiality and privacy vulnerabilities deep learning models have.
Our research work first explores deep learning-assisted cache side-channel attacks and introduces innovative countermeasures grounded in the principles of adversarial samples against deep learning. We first design a novel high-frequency cache monitor, which runs concurrent to the victim execution and collects run-time timing traces, while previous cache monitors are only able to collect timing samples. Such timing traces facilitate follow-on non-profiled Differential Deep Learning Analysis (DDLA) for secret retrieval. We also propose a novel countermeasure against the new DDLA, leveraging the concept of adversarial examples, which deliberately introduces obfuscation operations in the victim program so as to generate ‘adversarial’ timing traces and therefore circumvent the follow-on DDLA.
The second part of the dissertation addresses the vulnerability of deep neural network (DNN) implementations and presents novel methodologies for enhancing user privacy. It introduces a technique for extracting deep learning models through software-based power side channels. By manipulating model inputs and leveraging the on-chip Intel Running Average Power Limit (RAPL) sensors reporting, the entire model parameters can be extracted when the model inference is executed on modern processors. To protect both the model confidentiality and the input privacy, this dissertation proposes to obfuscate the model inputs while preserving the end-to-end functionality. It introduces an encoder to transform the inputs before feeding the DNN model, and appends a decoder after the model outputs to recover the intended results. The approach, compared to traditional encryption or masking techniques, is more efficient and can effectively protect both user privacy and model confidentiality.
The overall goal of the dissertation is to further investigate the power of deep learning in SCA and countermeasure and safeguard secure DNN implementations.

Computer Vision with Small Data (CV4Smalls): A Focus on Infants and Endangered Animals
2024-01-03 - 2024-01-07
Prof. Sarah Ostadabbas is the general chair of the inaugural workshop on Computer Vision with Small Data (CV4Smalls): A Focus on Infants and Endangered Animals. This workshop is hosted as part of the 2024 Winter Conference on Applications of Computer Vision (WACV) and promises to be a platform for groundbreaking research and insightful discussions. The workshop will take place in person between January 3-7, 2024 in Waikoloa, Hawaii, USA.
Researchers and practitioners are invited to submit original research papers, not exceeding 8 pages, adhering to the WACV template https://lnkd.in/efsp_8qx. Workshop papers will be included in IEEE Xplore, and will be indexed separately from the main conference papers. Submitted papers will undergo a rigorous peer-review double-blind process. For more information please contact: ostadabbas@ece.neu.edu
Important Dates:
• Paper Submission Deadline: 11th October, 2023
• Notification of Acceptance: 9th November, 2023
• Camera-Ready Deadline: 19th November, 2023
• Workshop Date: 3-7th January, 2024

Chemical Engineering Spring Seminar Series: Professor Hongfei Lin
2024-01-17 12:00 - 13:00 | 103 Churchill, 103 Churchill Hall, 360 Huntington Ave, Boston, MA, 02115, United States
Towards Holistic Approach for Decarbonizing Energy System
Decarbonizing the energy system is essential for mitigating climate change by replacing fossil fuels with alternative sources emitting significantly less carbon dioxide. Recognizing that no single alternative energy source can meet global demand, our approach involves utilizing multiple sources for a future carbon-neutral energy system. We focus on developing highly selective and efficient catalytic processes to convert diverse carbon feedstocks, including renewable and waste carbons. In this seminar, I will showcase our groundbreaking biphasic tandem catalytic processes, achieving exceptional carbon-atom efficiencies in converting renewable biomass into biofuels. Additionally, our innovative sequential catalytic process enables highly selective deconstruction of mixed waste plastics into valuable monomers and fuels. The presentation will also delve into the synergy of integrating direct air capture of CO2 for its utilization in producing value-added carbon-neutral products. Ultimately, our research aims to implement a holistic approach, decarbonizing the energy system, and establishing a sustainable supply of low-carbon intensity chemicals, materials, and fuels from renewable and waste carbon resources.

Dr. Hongfei Lin is a Professor at the Voiland School of Chemical Engineering and Bioengineering at Washington State University and Chief Scientist in the Energy and Environment Directorate at Pacific Northwest National Laboratory. He earned his B.E. and M.S. degrees from Tsinghua University, completed his Ph.D. in Chemical Engineering at Louisiana State University, and further honed his expertise as a postdoctoral fellow at the University of California, Santa Barbara. With nearly two decades of multidisciplinary research experience, Dr. Lin focuses on catalysis and sustainability, particularly in developing novel catalytic processes to derive value-added fuels and chemicals from renewable and waste carbon resources. His commitment to a sustainable, low-carbon, circular economy is evident through his numerous publications, multiple patents, and extensive support from entities such as DOE, NSF, and USDA. Dr. Lin actively contributes to the academic community, serving on the international advisory board of Energy Technology, the editorial board of Advanced Composites and Hybrid Materials, and previously as the Program Chair of the Energy and Fuels Division of the American Chemical Society.

BioE Faculty Search Seminar
2024-01-17 12:00 - 13:00
Join the Department of Bioengineering for weekly faculty seminars in 458 Richards Hall (unless otherwise stated).
1/17: Justin Randall, PhD
1/24: Jean-Baptiste Lugagne, PhD
1/31: Tianqi Song, PhD
2/7: Katheryn Rothenberg, PhD
2/14: Jae-Won Shin, PhD
2/21: Heidi Klumpe, PhD
Friday, 3/1, in International Village 019: Kolade Adebowale, PhD

LinkedIn, CV, Resume: A CommLab Workshop Series
2024-01-17 14:00 - 15:00 | 206 Egan, 360 Huntington Ave, Boston, MA, 02115, United States
Join our empowering LinkedIn, CV, and Resume Workshop series any Wednesday from 2 pm to 3 pm ET. This collaborative space offers valuable tips and peer feedback to enhance your online profile and professional presence. This is a community learning initiative, and together, we strive to make our profiles better. Join this hybrid workshop series either in person in room 206 Egan or through Zoom.

Mock Interview: A CommLab Workshop Series
2024-01-18 14:00 - 15:00 | 206 Egan, 360 Huntington Ave, Boston, MA, 02115, United States
Join the CommLab every Thursday from 2-3pm ET, we’ll delve into the intricacies of interviews, unveiling effective tricks and preparation strategies for any interview scenario. Engage in an interactive setting as we dissect the overall interview experience, discuss common interview scenarios, and share insights on what to do during critical moments. Join this hybrid workshop series either in person in room 206 Egan or through Zoom.

Northeastern NAI annual event
2024-01-18 17:00 - 19:00 | Raytheon Amphitheater (240 Egan), 360 Huntington Ave, 240 Egan, Boston, MA, 02115, United States
Join us for the Northeastern NAI annual meeting, a must-attend event for AI innovation enthusiasts! This in-person gathering will take place at the Egan Research Center, Forsyth Street, Boston, MA, USA. Get ready to hear from an AI expert in the field and an IP patent lawyer in a room filled with passionate individuals who share your love for innovation. This event is a unique opportunity to network, learn from industry experts, and explore groundbreaking ideas. Don’t miss out on this exciting event that promises to inspire and ignite your creativity. Mark your calendars and get ready for an unforgettable experience at the Northeastern NAI annual meeting.

Global Co-op Information Session
2024-01-18 17:30 - 18:30
Join the College of Engineering Global Co-op team in learning about global co-op opportunities for Summer II/ Fall 2024!
Topics discussed will include:

Search techniques and global positions in your field
Locations that students have co-oped in the past
What to consider when interested in a global co-op
Information on logistics, including visas and housing
Tips and resources for self-developing global positions

Attendance to one of these information sessions is required if you plan to do a global co-op in Summer II/ Fall 2024.
Location: Renaissance Park 909. Register on NUworks.
Please reach out to Kristina Hubbard, Global Co-op Coordinator, k.hubbard@northeastern.edu for additional information.

Towards Personalized Medicine in Speech Motor-Impaired Populations
2024-01-19 11:00 - 12:00
The Bouve College of Health Sciences cordially invites you to attend a presentation by SoCRS Faculty Candidate
Hannah Rowe, PhD, CCC-SLP
Towards Personalized Medicine in Speech Motor-Impaired Populations
Abstract: An estimated 130 million adults worldwide live with a speech motor disorder (i.e., difficulties with coordination, strength, and precision of the muscles involved in speech), which significantly impacts their quality of life. However, substantial heterogeneity across clinical populations and limited mechanistic understanding of speech motor control contribute to diagnostic challenges and a lack of targeted treatments. This job talk describes my long-term research goals to further the field of personalized medicine in speech motor-impaired populations, with a particular focus on individuals with neurodegenerative diseases and adults who stutter. I will talk about using behavioral, neurological, and physiological measures to develop clinical phenotypes for (1) informing diagnostic decision-making and (2) guiding individualized treatment. Towards my first research aim, I will highlight my dissertation work, which explores the efficacy of acoustic-based phenotypes for characterizing and classifying divergent neurodegenerative diseases. Towards my second research aim, I will highlight a project exploring the neural correlates of different disfluency types in adults who stutter, which served as pilot data for a recent grant submission on feedback- and feedforward-based phenotypes of stuttering. I will conclude by discussing future directions that support both aims of my research program.
Friday, January 19, 2024
11:00am-12:00pm
107 Robinson Hall

Graduate School of Engineering Campus Tour (In Person)
2024-01-20 10:00 - 11:00
Want to learn more about the Graduate School of Engineering on the Boston campus? Then we welcome you to sign up for a Graduate School of Engineering campus tour! Led by one of our expert Graduate Student Ambassadors, we’ll show you key locations on campus, in addition to resources specific to Engineering, and answer your questions about Boston. Please complete the registration form linked below to select the date and time that works best for you. Tours are open to both newly admitted and prospective students. We can’t wait to meet you!

CHE PhD Dissertation Defense: Jiaming Xu
2024-01-23 10:00 - 11:00
PhD Dissertation Defense: Molecular simulations of confined deep eutectic solvents for gas separations and liposomes for drug delivery
Location: ISEC 332 & Microsoft Teams
Abstract: This dissertation leverages molecular dynamics simulations to explore the properties of nanoscale materials and interfaces involving gases, liquids and solids, traversing the realms of environmental and biological science. This work not only demonstrates the expansive applicability of MD simulations across various scientific disciplines but also highlights their capability to provide profound insights into diverse scientific phenomena. In the segment dedicated to deep eutectic solvents, our study investigates the behavior of ethaline (mixtures of choline chloride with ethylene glycol at different molar ratios) confined in graphite and titania (rutile) slit pores, measuring 2 nm and 5 nm in width. This research aims to address the high viscosity issue prevalent in these solvents when saturated with CO2. The results reveal that modifications in the ethylene glycol ratio, variations in pore sizes, and the choice of pore wall materials significantly affect the efficiency of CO2/CH4 separation. These findings offer a deeper understanding of how molecular interactions and structural changes in confined spaces can influence the physical properties of DES.
The dissertation also delves into the domain of liposomes (nanoparticles formed by a lipid bilayer encapsulating an aqueous core), examining the influence of lipid composition and the integration of two distinct small-molecule hydrophobic drugs on their mechanical, spatial, and fluid properties. The study encompasses an analysis of the effects of acyl chain saturation and length, diverse lipid headgroups, and drug incorporation. Experimental validations, conducted in collaboration with Prof. Auguste’s laboratory, support our simulation findings. We discovered that lipids with short-saturated acyl chains and varied headgroups alter the lipid bilayer packing, resulting in decreased liposome stiffness, which has been shown promoted drug delivery efficiency. Additionally, specific drug substances were observed to lower interaction energies within the lipid matrix, which consequently reduces stiffness and enhances lipid molecule diffusion. This segment of the dissertation provides crucial insights into the design of liposomal formulations, particularly for drug delivery purposes, by demonstrating how lipid structure and drug interactions can be manipulated to optimize liposome properties. Overall, this dissertation underscores the versatility of molecular dynamics simulations in elucidating complex material behaviors and offers valuable contributions to the various engineering fields.

CHE PhD Dissertation Defense: Shicheng Yang
2024-01-24 10:00 - 12:00
PhD Dissertation Defense: Drug Delivery Systems in Oncology: From Polymeric Implants to Nanomedicine Approaches
Shicheng Yang
Location: Hastings Hall 107 & Zoom
Abstract: Molecular inhibitors, including PARP inhibitor talazoparib, CDK inhibitor dinaciclib, and docetaxel, are critical in precision cancer therapy, offering novel therapeutic options for a range of cancers. While demonstrating potent activity as monotherapy or in combination in both preclinical and clinical settings, challenges such as drug resistance and off-target toxicity persist with these small molecule drugs. To mitigate these issues, innovative formulation strategies using implants or nanoparticles have been explored. These formulations are designed to alter drug uptake pathways, resist the emergence of drug resistance, and minimize direct contact with healthy tissues, thereby reducing toxicity. This thesis encompasses several nanotechnology approaches in formulating chemotherapy agents and their application across various cancers, including breast, ovarian, pancreatic, lung, and prostate.
In the context of ovarian cancer, known for its high mortality rate within the realm of female reproductive system cancers, more than 15% of cases involve defective BRCA-mediated homologous recombination repair pathways. Talazoparib, a PARP inhibitor, has been hindered in its clinical application due to severe systemic side effects. The development of a novel TLZ-loaded PLGA implant (InCeT-TLZ) is reported, designed for sustained release over 25 days directly into the peritoneal cavity, targeting BRCA-mutated metastatic ovarian cancer. Results from in vivo experiments indicated a doubling of survival in the InCeT-TLZ treated group compared to controls, with no significant toxicity observed in surrounding peritoneal organs. This suggests that localized and sustained delivery of Talazoparib can enhance therapeutic efficacy without significant toxicity. Additionally, the potential of combining CKD inhibitor and PI3K inhibitor with InCeT-TLZ to counteract acquired PARPi resistance was demonstrated in vitro, indicating a promising approach for enhanced ovarian cancer treatment.
While the biodegradable PLGA implants showed potency, the conventional solvent-based fabrication methods used to synthesize these implants, however, the use of toxic organic solvent and its safety issue pose difficulties for translation to clinical use. To address these challenges, a scalable, solvent-free hot-melt extrusion process was introduced for producing PLGA implants iii with docetaxel. This process ensures uniform dispersion of clinically relevant concentrations of the drug without requiring organic solvents. Results showed the bioactivity of incapsulated docetaxel was maintained during fabrication and controlled degradation, enhancing tumor growth inhibition capabilities both in vitro and in vivo. The implants, when used intratumorally, act as both radiosensitizers and continuous chemotherapy sources, suitable for scale-up in compliance with Good Manufacturing Practices (GMP).
Furthermore, the combination of talazoparib and dinaciclib has been studied to overcome PARPi resistance in tumors. The short blood circulation time of dinaciclib and the high toxicity of combination therapies pose significant challenges. Nanomedicine formulations have been developed to address these issues, creating a nano-cocktail of talazoparib (nTLZ) and dinaciclib (nDCB) to enhance therapeutic efficacy at lower doses. The study showed that these nanoformulations effectively infiltrate tumor cells, with synergistic effects observed in both BRCAmutant and BRCA wild-type cancer strains, particularly sensitizing BRCA wild-type cells to PARPi therapy. This approach demonstrates the potential of nanoformulations in broadening the applicability and enhancing the efficacy of combination cancer therapies.
